Evolving Ensembles with TPOT

Detalhes bibliográficos
Autor(a) principal: Betancourt, Camila Andrea Sarmiento
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/149817
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Evolving Ensembles with TPOTMachine learningEnsemblesGenetic programmingTPOTDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceMachine learning has become popular in recent years as a solution to various problems such as fraud detection, weather prediction, improve diagnosis accuracy, and more. One of its goals is to find the model that best explains the problem. Among the several alternatives on how to accomplish that, significant attention has been laid on the matter of accuracy using stacking ensembles: the objective is to produce a more accurate prediction by combining the predictions of various estimators. This model has often been exhibiting a superior performance in contrast to its single counterparts. Because the process of choosing the best model for a given problem can be time-consuming, a necessity to automatize the machine learning process has emerged. Different tools allow this, including TPOT, a Python library that uses genetic programming to optimize the machine learning process, evolving pipelines randomly created until the best one is found, or a previously fixed maximum number of generations for the given problem is reached. Genetic programming is a field of machine learning that uses evolutionary algorithms to generate new computer programs, and it has been shown successful in quite a few applications. TPOT uses several machine learning algorithms from the Sklearn Python library. It also features some ensembles, such as Random Forest or AdaBoost. Currently, stacking ensembles are not implemented yet on TPOT, and, considering its current accuracy rates, the objective of this thesis is to implement stacking ensembles in TPOT. After we implemented stacking ensembles successfully in TPOT, we performed some experiments with different datasets and noticed that for almost all of them, TPOT has comparable performance to TPOT with stacking ensembles. Also, we observed that, when using the light dictionary version of TPOT, the results of the Stacking configuration improved for two datasets since it used weaker learners.Vanneschi, LeonardoRUNBetancourt, Camila Andrea Sarmiento2023-02-28T18:37:08Z2023-01-262023-01-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/149817TID:203239059enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:31:42Zoai:run.unl.pt:10362/149817Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:52.865462Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Evolving Ensembles with TPOT
title Evolving Ensembles with TPOT
spellingShingle Evolving Ensembles with TPOT
Betancourt, Camila Andrea Sarmiento
Machine learning
Ensembles
Genetic programming
TPOT
title_short Evolving Ensembles with TPOT
title_full Evolving Ensembles with TPOT
title_fullStr Evolving Ensembles with TPOT
title_full_unstemmed Evolving Ensembles with TPOT
title_sort Evolving Ensembles with TPOT
author Betancourt, Camila Andrea Sarmiento
author_facet Betancourt, Camila Andrea Sarmiento
author_role author
dc.contributor.none.fl_str_mv Vanneschi, Leonardo
RUN
dc.contributor.author.fl_str_mv Betancourt, Camila Andrea Sarmiento
dc.subject.por.fl_str_mv Machine learning
Ensembles
Genetic programming
TPOT
topic Machine learning
Ensembles
Genetic programming
TPOT
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2023
dc.date.none.fl_str_mv 2023-02-28T18:37:08Z
2023-01-26
2023-01-26T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/149817
TID:203239059
url http://hdl.handle.net/10362/149817
identifier_str_mv TID:203239059
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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